1 People

Faculty

Ph.D. Students

Former collaborators

2 Research

2.1 Simultaneous Localization and Map Building (SLAM)

State estimation techniques are developed for the simultaneous localization and map building (SLAM) problem. Both deterministic (set-membership) and probabilistic descriptions of the uncertainty are considered. Different environment representations are adopted (pointwise landmarks and linear features). The developed algorithms are suitably extended to the multi-robot scenario.

Probabilistic techniques

2.2 Collective Motion of Multi-Agent Systems

This research line deals with the coordination of multi-agent systems. A decentralized control law for the collective circular motion is developed and its stability properties are analyzed. Practical issues like collision avoidance and the limited field of view of the sensors are explicitly taken into account. The proposed control law is validated on real robots. Additionally, a remote lab for experimenting with small LEGO vehicles is available.

2.3 Opinion Dynamics and Consensus

The asymptotic behavior of threshold models used to describe the evolution of opinion dynamics and the formation of collective actions in social networks is studied. The proposed model introduces a parameter accounting for the level of self-confidence of the agents, which affects the dynamic evolution of the threshold and in turn the way the agents make their decision. The impact that the network topology has on the asymptotic behavior of the system is studied both analytically and via numerical simulation.

A related research line concerns the performance of consensus protocols in the presence of bounded measurement errors. Both static and dynamic weights are considered. Bounds and the maximum deviation from consensus are derived in terms of the structure of the weight matrix and the maximum magnitude of the measurement errors.

3 Videos

3.1 Single-robot SLAM using linear features

A Pioneer 3AT mobile robot performing SLAM in our former lab, under Bernardino Fungai's "Last Supper" fresco (15th century) [c4,c5]. The non orthogonal walls in the final map are actually like this old building is made!

This video shows the effect of loop closure in a simulated environment. Notice how the map is registered when the robot recognizes already visited places.

3.2 Multi-robot SLAM using M-Space feature representation

This video shows the a run of the multi-robot SLAM agorithm, using linear features and M-Space representation [j9,c12]. Two Pioneer 3AT robots explore the second floor of our Department (about 3000 m2), starting from different unknown locations. When they meet, relative measurements are taken and the local maps are merged together.

3.3 Circular motion of nonholonomic vehicles

The first video shows a simulation of four unicycles tracking a moving target while rotating around it, resulting from the application of the control law proposed in [j5,c6]. The second video shows an experiment of circular motion about a stationary target performed with a team of LEGO Mindstorms robots [j6,c8,c10].

3.4 Remote lab for multi-agent systems

The first video shows how to remotely perform a multi-robot experiment through the Automatic Control Telelab, developed at the University of Siena [j10,c11,c13,c14,c15]. The second video shows the actual robots moving during a motion coordination experiment and then autonomously returning to the recharge station.

3.5 The Headed Social Force Model

The following videos show the behaviour of the Headed Social Force Model [j10,c17].

A public repo on GitHub containing some MATLAB and Python code is available here.

The Nonholonomic Behavior

The following two simple case studies involve a single pedestrian and are aimed at showing the high fidelity of the HSFM in reproducing the trajectories of pedestrians moving in free space according to a nonholonomic behavior.

The Adaptive Behavior

Here we consider different experiments, involving a number of pedestrians ranging from 20 to 200. The purpose is to illustrate the ability of the HSFM to automatically adapt the generated trajectories to the external context, smoothly relaxing the nonholonomic constraints as the pedestrian density increases or unexpected obstacles come into play.

A Visit at the Museum

The following is a more articulated case study. A group of 10 people visiting a museum together is considered. The focus of this study is to show how the group force introduced in the HSFM originates trajectories preserving the cohesion of the group.

Sensitivity analysis

In the following simulations are shown the effect of parameter variations on the generated trajectories.